我们首次建议使用基于多个实例学习的无卷积变压器模型，称为多个实例神经图像变压器（Minit），以分类T1Weighted（T1W）MRIS。我们首先介绍了为神经图像采用的几种变压器模型。这些模型从输入体积提取非重叠的3D块，并对其线性投影进行多头自我注意。另一方面，Minit将输入MRI的每个非重叠的3D块视为其自己的实例，将其进一步分为非重叠的3D贴片，并在其上计算了多头自我注意力。作为概念验证，我们通过训练模型来评估模型的功效，以确定两个公共数据集的T1W-MRIS：青少年脑认知发展（ABCD）和青少年酒精和神经发展联盟（NCANDA）（NCANDA） 。博学的注意力图突出了有助于识别脑形态计量学性别差异的体素。该代码可在https://github.com/singlaayush/minit上找到。

State space models (SSMs) have demonstrated state-of-the-art sequence modeling performance in some modalities, but underperform attention in language modeling. Moreover, despite scaling nearly linearly in sequence length instead of quadratically, SSMs are still slower than Transformers due to poor hardware utilization. In this paper, we make progress on understanding the expressivity gap between SSMs and attention in language modeling, and on reducing the hardware barrier between SSMs and attention. First, we use synthetic language modeling tasks to understand the gap between SSMs and attention. We find that existing SSMs struggle with two capabilities: recalling earlier tokens in the sequence and comparing tokens across the sequence. To understand the impact on language modeling, we propose a new SSM layer, H3, that is explicitly designed for these abilities. H3 matches attention on the synthetic languages and comes within 0.4 PPL of Transformers on OpenWebText. Furthermore, a hybrid 125M-parameter H3-attention model that retains two attention layers surprisingly outperforms Transformers on OpenWebText by 1.0 PPL. Next, to improve the efficiency of training SSMs on modern hardware, we propose FlashConv. FlashConv uses a fused block FFT algorithm to improve efficiency on sequences up to 8K, and introduces a novel state passing algorithm that exploits the recurrent properties of SSMs to scale to longer sequences. FlashConv yields 2$\times$ speedup on the long-range arena benchmark and allows hybrid language models to generate text 1.6$\times$ faster than Transformers. Using FlashConv, we scale hybrid H3-attention language models up to 1.3B parameters on the Pile and find promising initial results, achieving lower perplexity than Transformers and outperforming Transformers in zero- and few-shot learning on a majority of tasks in the SuperGLUE benchmark.

Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasive and the laboratory analysis can be slow or inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool which is quick and simple to acquire. However, the problem of estimating continuous electrolyte concentrations directly from ECGs is not well-studied. We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations. Methods: We explore the use of deep neural networks (DNNs) for this task. We analyze the regression performance across four electrolytes, utilizing a novel dataset containing over 290000 ECGs. For improved understanding, we also study the full spectrum from continuous predictions to binary classification of extreme concentration levels. To enhance clinical usefulness, we finally extend to a probabilistic regression approach and evaluate different uncertainty estimates. Results: We find that the performance varies significantly between different electrolytes, which is clinically justified in the interplay of electrolytes and their manifestation in the ECG. We also compare the regression accuracy with that of traditional machine learning models, demonstrating superior performance of DNNs. Conclusion: Discretization can lead to good classification performance, but does not help solve the original problem of predicting continuous concentration levels. While probabilistic regression demonstrates potential practical usefulness, the uncertainty estimates are not particularly well-calibrated. Significance: Our study is a first step towards accurate and reliable ECG-based prediction of electrolyte concentration levels.

In this work, we give efficient algorithms for privately estimating a Gaussian distribution in both pure and approximate differential privacy (DP) models with optimal dependence on the dimension in the sample complexity. In the pure DP setting, we give an efficient algorithm that estimates an unknown $d$-dimensional Gaussian distribution up to an arbitrary tiny total variation error using $\widetilde{O}(d^2 \log \kappa)$ samples while tolerating a constant fraction of adversarial outliers. Here, $\kappa$ is the condition number of the target covariance matrix. The sample bound matches best non-private estimators in the dependence on the dimension (up to a polylogarithmic factor). We prove a new lower bound on differentially private covariance estimation to show that the dependence on the condition number $\kappa$ in the above sample bound is also tight. Prior to our work, only identifiability results (yielding inefficient super-polynomial time algorithms) were known for the problem. In the approximate DP setting, we give an efficient algorithm to estimate an unknown Gaussian distribution up to an arbitrarily tiny total variation error using $\widetilde{O}(d^2)$ samples while tolerating a constant fraction of adversarial outliers. Prior to our work, all efficient approximate DP algorithms incurred a super-quadratic sample cost or were not outlier-robust. For the special case of mean estimation, our algorithm achieves the optimal sample complexity of $\widetilde O(d)$, improving on a $\widetilde O(d^{1.5})$ bound from prior work. Our pure DP algorithm relies on a recursive private preconditioning subroutine that utilizes the recent work on private mean estimation [Hopkins et al., 2022]. Our approximate DP algorithms are based on a substantial upgrade of the method of stabilizing convex relaxations introduced in [Kothari et al., 2022].

Robots have been steadily increasing their presence in our daily lives, where they can work along with humans to provide assistance in various tasks on industry floors, in offices, and in homes. Automated assembly is one of the key applications of robots, and the next generation assembly systems could become much more efficient by creating collaborative human-robot systems. However, although collaborative robots have been around for decades, their application in truly collaborative systems has been limited. This is because a truly collaborative human-robot system needs to adjust its operation with respect to the uncertainty and imprecision in human actions, ensure safety during interaction, etc. In this paper, we present a system for human-robot collaborative assembly using learning from demonstration and pose estimation, so that the robot can adapt to the uncertainty caused by the operation of humans. Learning from demonstration is used to generate motion trajectories for the robot based on the pose estimate of different goal locations from a deep learning-based vision system. The proposed system is demonstrated using a physical 6 DoF manipulator in a collaborative human-robot assembly scenario. We show successful generalization of the system's operation to changes in the initial and final goal locations through various experiments.

Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is three-dimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.

Dynamic movement primitives are widely used for learning skills which can be demonstrated to a robot by a skilled human or controller. While their generalization capabilities and simple formulation make them very appealing to use, they possess no strong guarantees to satisfy operational safety constraints for a task. In this paper, we present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace. We present a formulation of a non-linear optimization to perturb the DMP forcing weights regressed by locally-weighted regression to admit a Zeroing Barrier Function (ZBF), which certifies workspace constraint satisfaction. We demonstrate the proposed CDMP under different constraints on the end-effector movement such as obstacle avoidance and workspace constraints on a physical robot. A video showing the implementation of the proposed algorithm using different manipulators in different environments could be found here https://youtu.be/hJegJJkJfys.

诸如DALL-E 2之类的生成模型可以代表放射学中人工智能研究的图像生成，增强和操纵的有希望的未来工具，前提是这些模型具有足够的医疗领域知识。在这里，我们证明DALL-E 2在零拍的文本到图像生成方面，学习了具有有希望的功能的X射线图像的相关表示，将图像的延续超出其原始边界或删除元素，尽管病理产生或CT，MRI和超声图像仍然受到限制。因此，即使事先需要对这些模型进行进一步的微调和适应，也需要使用生成模型来增强和生成放射学数据似乎是可行的。

使用量子卷积神经网络（QCNN）的机器学习在量子和经典数据分类中都取得了成功。在先前的研究中，在少数参数制度中，在相同的训练条件下，QCNN的分类准确性比其经典对应物具有更高的分类精度。但是，由于量子电路的大小有限，因此很难检查大规模量子模型的一般性能，这可以在不久的将来可靠地实施。我们建议转移学习是在嘈杂的中间量子量子时代利用小QCNN的有效策略。在经典到量词转移学习框架中，QCNN可以通过使用预训练的经典卷积神经网络（CNN）来解决复杂的分类问题，而无需大规模量子电路。我们对QCNN模型进行了数值模拟，并在转移学习下对MNIST数据分类进行了各种量子卷积和汇总操作，其中经典的CNN经过了时尚持续数据的培训。结果表明，在相似的训练条件下，从经典到量子CNN的转移学习比纯粹的经典转移学习模型要好得多。

某些语言允许在某些情况下省略参数。然而，人类语言理解者可靠地推断出这些零代词的预期参考人，部分原因是他们建立了对哪些参考人更有可能的期望。我们询问神经语言模型是否也提取了相同的期望。我们测试了12种当代语言模型是否显示出反映人类行为的期望，这些句子暴露于Carminati（2005）中意大利五个行为实验中的零代词中。我们发现三个模型-XGLM 2.9B，4.5B和7.5B-从所有实验中捕获人类行为，而其他实验则成功地对某些结果进行了建模。该结果表明，人类对核心的期望可以从接触语言中得出，并且还指示了语言模型的特征，使他们能够更好地反映人类的行为。